When to Hire Dedicated AI Engineers Vs Use a Managed AI Team

When to Hire Dedicated AI Engineers Vs Use a Managed AI Team

As AI becomes increasingly transformative of business, one of the initial decisions leaders must make is whether to hire AI Engineers or opt for a managed AI team. Either approach is effective, but it will depend upon your objectives, budget and long-term plan.  

Establishing in-house teams might be costly and laborious, since there are over 1.6 million AI roles out there globally, and there are a lot of lacking skills. At the same time, the use of managed AI services is surging, outpacing the growth rate by over 30% each year. 

In-house teams have control and deep integration while managed services are fast and flexible. Knowing the compromise early will save time and resources. In this blog, we’ll look at when it makes sense to invest in dedicated talent and when outsourcing AI capabilities is a better and efficient way. 

Understanding the Surge in AI Engineer Demand 

As companies transition from the phase of testing AI to integrating it into their business processes, the need for AI engineers is also growing rapidly. A Gartner report projects that as many as 75% of enterprises will move at least some of their AI development to providers outside of the enterprise by 2026.  

In the meantime, there are massive constraints on talent in the talent market. AI job demand is outpacing supply: Over 1.6 million AI job openings in the world, and there’s a lack of qualified talent. All this has contributed to an imbalance, which has resulted in higher salaries, longer hiring cycles, and greater use of alternative delivery models.  

Not to mention, the adoption of AI is not restricted to tech companies anymore. The need for MLOps, generative AI and model deployment is growing, as an increasing number of industries are beginning to take advantage of AI for a range of purposes including marketing, operation, and product development. With this evolution, AI talent is shifting from a specialised need to a business imperative, prompting organisations to question their policy of building, outsourcing, or using a hybrid strategy to achieve their AI objectives. 

What is a Dedicated AI Engineer Team? 

A Dedicated AI Engineer Team is a team of internal AI experts, such as machine learning engineers, data scientists, AI architects and MLOps specialists. They are tasked with the development, design, deployment and improvement of AI solutions that are tailored to your business. They work with the internal teams to get to know you, your product, information and future plans. 

Pros of Hiring a Dedicated AI Team 

• Full Control & Ownership
  AI models, data, and IP remain under full control of the user. 

• Deep Business Integration
  Engineers know your workflows and are able to offer tailored solutions. 

• Long-Term Capability Building
  Builds internal expertise and minimises reliance on external suppliers. 

• Continuous Improvement
  More flexible iteration, optimisation and scaling of AI systems over time. 

Cons of Hiring a Dedicated AI Team 

• High Upfront Costs
  There’s a lot of investment that’s needed for hiring, salaries, infrastructure, and tools. 

• Talent Acquisition Challenges
  The shortage of AI team members has resulted in extended hiring timelines. 

• Slower Time-to-Market
  Hiring an entire team from the ground up can cause the project to be slower to start. 

• Ongoing Management Overhead
  Needs leadership, training and retention. 

What is a Managed AI Team? 

A Managed AI Team is an external team of AI experts responsible for the development, deployment and maintenance of your AI solutions. Rather than developing an internal team, you work with specialists on a project basis, without hiring and training the staff. They are often offered as end-to-end services, which is great for businesses seeking fast implementation of AI. 

Pros of Partnering with Managed AI Team 

• Faster Time-to-Market
  Expertise and frameworks are pre-built, allowing for rapid project launches and execution. 

• Lower Upfront Investment
  No need to hire, onboard, or set up infrastructure. 

• Access to Diverse Expertise
  Secure an industry and use-case cross-functional team. 

• Scalability & Flexibility
  Scale up or down resources as needed for a project. 

Cons of partnering with Managed AI Team 

• Less Control
  Lack of visibility and control over development processes and decisions. 

• Dependency on Vendor
  Long-term dependency can have an impact on internal capability development. 

• Data Security & Compliance Risks
  When sensitive information is provided to external parties, it can pose risks if not handled correctly. 

• Customisation Limitations
  Solutions might not be as customised as in-house solutions. 

When to Hire Dedicated AI Engineers 

1. Building Proprietary systems  

Having an in-house team is crucial when creating custom, proprietary AI solutions that are integral to your product or service. You can collaborate with internal systems with dedicated engineers to develop highly customised models that fit the internal systems seamlessly into the broader ecosystem. 

1. You Have a Long-Term AI Roadmap 

For organisations looking to embark on AI projects over a period of years, a dedicated team guarantees continuity and alignment with business objectives. AI can be a long-term effort, with in-house engineers constantly refining models, testing new strategies, and scaling solutions. 

1. You Work with Proprietary Data 

With control over their data, businesses can ensure that they are following data security regulations and standards. This decreases the risk in giving out confidential data to external vendors. 

1. You Need Deep System Integration 

Having an in-house team can help streamline collaboration and speed up iterations when AI solutions need to be tightly connected to internal systems, such as CRMs, ERPs, or custom-built platforms. Dedicated engineers can collaborate closely with other departments to guarantee seamless implementation and optimisation. 

1. You Want to Build Internal AI Capability 

Organisations aiming to become AI-driven need more than outsourced solutions, they need internal expertise. A dedicated team supports your capacity to create knowledge, frameworks and processes that will enhance your capacity to innovate and de-dependency from external providers. 

When to Use a Managed AI Team 

1. Need Faster Time-to-Market 

A managed AI team is the answer if you have to launch AI products, prototypes, MVP, or production features quickly. They introduce your knowledge, ready-made frameworks, and proven workflows, to get your idea from concept to execution in much less time than you would need to hire and train an employee. 

1. Early-Stage AI Adoption 

Outsourcing is viable when you’re new to AI and testing out use cases or ROI. It can be used to trial new concepts, test them out, and discover what’s effective without the requirement for any permanent contracts or infrastructure. 

1. Limited Budget or Resources 

If you don’t have the budget to have a full team of AI experts on staff, a managed service can be a great solution. You do not have to spend money on recruitment, salaries, training and infrastructure, and still have access to qualified professionals. 

1. Project-Based or Short-Term Needs 

For one-time projects such as building a chatbot, automating workflows, or developing a proof of concept a managed AI team is ideal. You can outsource experts instead of having a dedicated team of experts and give them a specific timeline and deliverables. 

1. Access to Specialised Expertise 

When your project requires niche skills such as Generative AI, computer vision, or MLOps, a managed AI team can easily put you in touch with the right experts. This can be very helpful when you don’t have a certain technical skill or experience in your own team. 

Hybrid Approach: The Best of Both Worlds

The optimal way of course is not to choose between one model or another; it is to use both models together for many businesses.  

A hybrid approach involves having a managed AI team that works on creating and launching your initial solutions and then eventually moving towards fully in-house by recruiting dedicated AI engineers. You’ll be able to get the speed and expertise of the outside teams early on, and the control and knowledge of the inside teams in the long-term.  

It provides a way to rapidly implement solutions at the beginning without intricate hiring procedures and scale your AI requirements without becoming overwhelmed. 

How the Hybrid Model Works 

Phase 1: Strategy & Prototype 

Using AI, a consulting team or managed team guides the roadmap, does the feasibility studies and develop the initial proof-of-concept (POC) projects. This ensures that you don’t work on the wrong problem and minimise risk early on. 

Phase 2: Development & Deployment
The external team collaborates closely with your internal stakeholders to develop and deploy production-ready AI solutions. The advantage of this stage is that it is executed quickly and there are skilled workers available. 

Phase 3: Knowledge Transfer 

The managed team documents processes and trains the internal employees before transition. This way, your team knows how to use, manage, and enhance the AI system. 

Phase 4: In-House Ownership
Once your internal capability matures, your dedicated AI engineers take over. They then scale, optimise, and innovate on their own, bringing AI much closer to their long-term business goals. 

Why This Approach Works? 

There is a balance between speed and cost of hires in the early stages, and a gradual build-up of expertise within the organisation. It helps to minimise risk, prevent the early construction of a large team, and produce a smoother transition to self-sufficiency.  

It’s the best strategy to follow if your company is at the beginning or the transition to using AI: 

• You are new to AI and require the assistance of professionals to begin. 

• You have limited internal technical resources initially 

• You want to test before scaling ROI use cases and AI use cases  

• You are looking to develop long-term in-house AI skills incrementally 

Conclusion 

The decision between choosing dedicated AI engineers and managed AI team depends on your business objectives and timeline. While dedicated teams offer long-term control and innovation, managed services provide speed and flexibility. There are examples of many organisations that are now combining both to scale efficiently.  

For beginners, having some guidance in finding AI developers for hire can help get you on the fast track without making a huge initial investment. As your needs increase you can incrementally establish your own capacity. The important thing to remember is to be flexible, set more clear objectives, and adjust your approach to AI as your company grows.